{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:31:05Z","timestamp":1781587865204,"version":"3.54.5"},"reference-count":70,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,9,6]],"date-time":"2023-09-06T00:00:00Z","timestamp":1693958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2021YFF1200902"],"award-info":[{"award-number":["2021YFF1200902"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["32270689, 62106008, and 62276002"],"award-info":[{"award-number":["32270689, 62106008, and 62276002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>Recent years have witnessed the explosive growth of interaction behaviors in multimedia information systems, where multi-behavior recommender systems have received increasing attention by leveraging data from various auxiliary behaviors such as tip and collect. Among various multi-behavior recommendation methods, non-sampling methods have shown superiority over negative sampling methods. However, two observations are usually ignored in existing state-of-the-art non-sampling methods based on binary regression: (1) users have different preference strengths for different items, so they cannot be measured simply by binary implicit data; (2) the dependency across multiple behaviors varies for different users and items. To tackle the above issue, we propose a novel non-sampling learning framework named<jats:underline>C<\/jats:underline>riterion-guided<jats:underline>H<\/jats:underline>eterogeneous<jats:underline>C<\/jats:underline>ollaborative<jats:underline>F<\/jats:underline>iltering (CHCF). CHCF introduces both upper and lower thresholds to indicate selection criteria, which will guide user preference learning. Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior. We further theoretically demonstrate that the optimization of Collaborative Metric Learning can be approximately achieved by the CHCF learning framework in a non-sampling form effectively. Extensive experiments on three real-world datasets show the effectiveness of CHCF in heterogeneous scenarios.<\/jats:p>","DOI":"10.1145\/3611310","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T15:41:27Z","timestamp":1690472487000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7987-3714","authenticated-orcid":false,"given":"Xiao","family":"Luo","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of California, Los Angeles, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3354-0973","authenticated-orcid":false,"given":"Daqing","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5915-4448","authenticated-orcid":false,"given":"Yiyang","family":"Gu","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0213-9957","authenticated-orcid":false,"given":"Chong","family":"Chen","sequence":"additional","affiliation":[{"name":"Terminus Group, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3817-6267","authenticated-orcid":false,"given":"Luchen","family":"Liu","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7388-4295","authenticated-orcid":false,"given":"Jinwen","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9809-3430","authenticated-orcid":false,"given":"Ming","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9143-1898","authenticated-orcid":false,"given":"Minghua","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Mathematical Sciences, Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5735-2910","authenticated-orcid":false,"given":"Jianqiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8232-5049","authenticated-orcid":false,"given":"Xian-Sheng","family":"Hua","sequence":"additional","affiliation":[{"name":"Terminus Group, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/7503.003.0010"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16515"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331192"},{"issue":"2","key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3373807","article-title":"Efficient neural matrix factorization without sampling for recommendation","volume":"38","author":"Chen Chong","year":"2020","unstructured":"Chong Chen, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. 2020. Efficient neural matrix factorization without sampling for recommendation. ACM Transactions on Information Systems 38, 2 (2020), 1\u201328.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5329"},{"key":"e_1_3_2_7_2","first-page":"1597","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In Proceedings of the International Conference on Machine Learning. 1597\u20131607."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2978618"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.330161"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2931327"},{"issue":"7","key":"e_1_3_2_11_2","article-title":"Adaptive subgradient methods for online learning and stochastic optimization.","volume":"12","author":"Duchi John","year":"2011","unstructured":"John Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research 12, 7 (2011), 2121\u20132159.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2019.00140"},{"key":"e_1_3_2_13_2","article-title":"Learning to recommend with multiple cascading behaviors","author":"Gao Chen","year":"2019","unstructured":"Chen Gao, Xiangnan He, Danhua Gan, Xiangning Chen, Fuli Feng, Yong Li, Tat-Seng Chua, Lina Yao, Yang Song, and Depeng Jin. 2019. Learning to recommend with multiple cascading behaviors. IEEE Transactions on Knowledge and Data Engineering 33, 6 (2019), 2588\u20132601.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/285"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2911489"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052639"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/345508.345545"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401072"},{"key":"e_1_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Wei Ju Zheng Fang Yiyang Gu Zequn Liu Qingqing Long Ziyue Qiao Yifang Qin Jianhao Shen Fang Sun Zhiping Xiao Junwei Yang Jingyang Yuan Yusheng Zhao Xiao Luo and Ming Zhang. 2023. A comprehensive survey on deep graph representation learning. arXiv:2304.05055. Retrieved from https:\/\/arxiv.org\/abs\/2304.05055","DOI":"10.1016\/j.neunet.2024.106207"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/502585.502627"},{"key":"e_1_3_2_24_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403226"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124317"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2984094"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2981237"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5894"},{"key":"e_1_3_2_32_2","article-title":"Be causal: De-biasing social network confounding in recommendation","author":"Li Qian","year":"2022","unstructured":"Qian Li, Xiangmeng Wang, Zhichao Wang, and Guandong Xu. 2022. Be causal: De-biasing social network confounding in recommendation. ACM Transactions on Knowledge Discovery from Data 17, 1 (2022), 1\u201323.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883090"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3041021.3054202"},{"issue":"1","key":"e_1_3_2_36_2","first-page":"1","article-title":"Bayesian additive matrix approximation for social recommendation","volume":"16","author":"Liu Huafeng","year":"2021","unstructured":"Huafeng Liu, Liping Jing, Jingxuan Wen, Pengyu Xu, Jian Yu, and Michael K. Ng. 2021. Bayesian additive matrix approximation for social recommendation. ACM Transactions on Knowledge Discovery from Data 16, 1 (2021), 1\u201334.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_2_37_2","doi-asserted-by":"crossref","unstructured":"Babak Loni Roberto Pagano Martha Larson and Alan Hanjalic. 2016. Bayesian personalized ranking with multi-channel user feedback. 10th ACM Conference on Recommender Systems RecSys 2016 . Association for Computing Machinery (ACM) 2016 361\u2013364.","DOI":"10.1145\/2959100.2959163"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403147"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2011.134"},{"key":"e_1_3_2_40_2","volume-title":"Proceedings of the AAAI Workshop","author":"Oard Douglas W.","year":"1998","unstructured":"Douglas W. Oard and Jinmook Kim. 1998. Implicit feedback for recommender systems. In Proceedings of the AAAI Workshop."},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00052"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.01.057"},{"key":"e_1_3_2_43_2","unstructured":"Yifang Qin Wei Ju Hongjun Wu Xiao Luo and Ming Zhang. 2023. Learning graph ODE for continuous-time sequential recommendation. arXiv:2304.07042. Retrieved from https:\/\/arxiv.org\/abs\/2304.07042"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539597.3570408"},{"key":"e_1_3_2_45_2","volume-title":"Proceedings of the UAI","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidtthieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In Proceedings of the UAI."},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3_1"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.3007330"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401969"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186154"},{"key":"e_1_3_2_50_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_2_51_2","first-page":"3698","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Wang Huizhao","year":"2019","unstructured":"Huizhao Wang, Guanfeng Liu, An Liu, Zhixu Li, and Kai Zheng. 2019. DMRAN: A hierarchical fine-grained attention-based network for recommendation. In Proceedings of the International Joint Conference on Artificial Intelligence. 3698\u20133704."},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00071"},{"key":"e_1_3_2_53_2","article-title":"Learning aspect-aware high-order representations from ratings and reviews for recommendation","author":"Wang Ke","year":"2022","unstructured":"Ke Wang, Yanmin Zhu, Haobing Liu, Tianzi Zang, and Chunyang Wang. 2022. Learning aspect-aware high-order representations from ratings and reviews for recommendation. ACM Transactions on Knowledge Discovery from Data 17, 1 (2022), 1\u201322.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467415"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6094"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401443"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9533645"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-00126-0_11"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE51399.2021.00179"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401445"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16576"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462972"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132911"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1172"},{"key":"e_1_3_2_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00090"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2779043"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401155"},{"issue":"2","key":"e_1_3_2_69_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3470659","article-title":"Hybrid variational autoencoder for recommender systems","volume":"16","author":"Zhang Hangbin","year":"2021","unstructured":"Hangbin Zhang, Raymond K. Wong, and Victor W. Chu. 2021. Hybrid variational autoencoder for recommender systems. ACM Transactions on Knowledge Discovery from Data 16, 2 (2021), 1\u201337.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741656"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/619"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3611310","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3611310","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:54Z","timestamp":1750287054000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3611310"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,6]]},"references-count":70,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1,31]]}},"alternative-id":["10.1145\/3611310"],"URL":"https:\/\/doi.org\/10.1145\/3611310","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,6]]},"assertion":[{"value":"2022-08-29","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-07-17","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-06","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}